Xiaoping Fan, Kunyang He, Wenliu Xu, Zengxiao Zou, Naijimuding Abudurexiti, Ge Wang, Chaojie Wang
Frozen elephant trunk (FET) was applied in the treatment of type A aortic dissection, enabling one-stage repair of the aortic arch and proximal descending aorta while promoting true lumen expansion. However, inadvertent FET deployment into the false lumen is a rare but catastrophic complication that may cause true lumen collapse and life-threatening visceral malperfusion, with limited reports on its endovascular management. Here, we present the case of a 44-year-old female who underwent Bentall procedure, total arch replacement, and FET implantation for acute type A aortic dissection at a local hospital. Postoperatively, she developed visceral malperfusion manifesting as abdominal distension, hepatic and renal dysfunction, and elevated lactate levels. Computed tomography angiography revealed that the FET stent had been inadvertently deployed into the false lumen, resulting in true lumen collapse. Emergency thoracic endovascular aortic repair was performed, establishing a “FET-false lumen-true lumen” pathway with an additional stent graft to redirect aortic flow into the true lumen. Computational fluid dynamics analysis showed that the postoperative hemodynamics have been significantly improved. The patient's organ function normalized, and she was discharged two weeks later, remaining asymptomatic at 8-month follow-up. Endovascular reintervention with additional stent grafting to establish a flow-redirection pathway is a feasible and effective strategy for true lumen collapse following inadvertent FET deployment into the false lumen. Prompt recognition, timely imaging confirmation, and immediate intervention are paramount to mitigating the risks associated with this serious complication.
Francesco Vitali, Giorgia Azzolini, Marco Zuin, Nicola Bianchi, Michele Malagù, Martina De Raffele, Anna Cantone, Federico Sanguettoli, Federico Gibiino, Cristina Balla, Elisabetta Tonet, Rita Pavasini, Gianluca Campo, Matteo Bertini
Sex-based differences in ACS presentation and outcomes have been observed, but limited information is available on sex-specific electrocardiogram (ECG) features and their prevalence in this context. The aim of this study was to compare the prevalence of different ECG patterns between sexes and their prognostic roles for in-hospital mortality in ACS patients. This prospective single-center study (NCT02438085)° was conducted from June 2018 to December 2019, including 841 consecutive patients. Data on baseline characteristics, ECG findings, treatments, and outcomes were collected. Statistical analyses were performed to determine differences in the prevalence of various ECG patterns and identify independent predictors of in-hospital mortality. Males more frequently presented with ST-segment elevation (51% vs. 43%, p = 0.031) and hyperacute T waves (13% vs. 7%, p = 0.006) than females. Moreover, females more often had a normal ECG at admission (15% vs. 9%, p = 0.01). In female STEMI negative T waves (50% vs. 38%, p = 0.025) were more common than in males. Hyperacute T waves were linked to a higher risk of in-hospital death in female STEMI cases (OR=6.25, 95% CI: 1.0 −39.7, p = 0.048). Additionally the development of left bundle branch block during hospitalization (OR=14.4, 95% CI: 1.48–14 p = 0.02) was a significant predictor of in-hospital mortality in the female NSTEMI subgroup. No ECG features were found to predict in-hospital mortality in male patients. Compared to males, females with ACS often showed less frequent and less extensive ST-segment abnormalities and were more likely to have a normal initial ECG at admission. These differences could impact clinical evaluation and prognosis. Recognizing these sex-related ECG differences may help optimize the management of ACS patients. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT02438085
Changfu Hu, Tao Bai, Xiao Qin, Kangsui Tang, Tao Peng, Qingbo Yang, Minkang Jiang, Lin Zhang
Prolonged length of stay (PLOS) after endovascular aortic repair (EVAR) for abdominal aortic aneurysm (AAA) is associated with increased complications, resource burden, and patient distress. Early identification of high-risk patients remains challenging. This study aimed to develop and externally validate a predictive model integrating clinical variables and three-dimensional deep learning (3D-DL) features from preoperative computed tomography angiography (CTA) to predict PLOS after EVAR. A total of 788 patients with AAA who underwent elective EVAR were enrolled from two hospitals. The internal training cohort consisted of 556 patients, and the external validation cohort included 232 patients. PLOS was defined as a LOS above the 75th percentile of the distribution. A clinical nomogram was developed using LASSO regression followed by multivariate logistic regression. Deep learning (DL) features were extracted from manually segmented computed tomography angiography (CTA) images using a 3D ResNet200 network, and LASSO regression was applied to select the most informative features. An ensemble model integrating the clinical predictors and the selected DL features was constructed. Model performance was assessed using receiver operating characteristic curves, calibration plots, and decision curve analysis. LASSO combined with multivariate logistic regression identified five independent clinical predictors: age, hemoglobin, albumin, maximum aneurysm diameter, and diabetes. The clinical model achieved an area under the curve (AUC) of 0.917 in the internal cohort and 0.944 in the external cohort. From the 3D ResNet200 network, 32 DL features were extracted. LASSO regression further selected two of these features. The ensemble predictive model, which integrated the clinical predictors and DL features, demonstrated improved discrimination, with an AUC of 0.971 in the internal cohort and 0.958 in the external cohort. Internal validation with 1,000 bootstrap resamples yielded optimism-corrected AUCs of 0.911 for the clinical model and 0.966 for the ensemble model. Subgroup analyses demonstrated consistent model performance across age and diabetes strata, while performance in the female subgroup warrants further evaluation in larger cohorts. This study developed an ensemble predictive model to predict PLOS following EVAR. The model demonstrated excellent discrimination and provided significant prognostic value beyond clinical variables alone. This nomogram may serve as a preoperative tool for risk stratification and resource planning in this population. Future studies should focus on prospective validation and clinical integration.
Hua Bai, Rigun A, Gang Zhang, Zhijie Guo, Dahai Bing, Yajiang Zhang, Peng Liu, Xi Liu
To investigate the prognostic value of three-vessel quantitative flow ratio (3V-QFR) in patients with borderline coronary lesions (QFR 0.75–0.80) over a 2-year period; to identify key variables using LASSO regression; and to construct and internally validate a personalized nomogram model for predicting major adverse cardiovascular events (MACE) over 2 years. This single-center retrospective cohort study included patients with coronary artery disease who were treated at Ordos Central Hospital from January 2021 to December 2024, underwent coronary angiography revealing at least one borderline lesion in a major coronary artery, and received a QFR assessment. Patients were followed for up to 24 months after discharge through outpatient visits, telephone follow-up, and electronic medical-record review. The primary endpoint was defined as the occurrence of MACE during the 2-year follow-up period. Restricted cubic spline (RCS) analysis was used to explore the nonlinear relationship between 3V-QFR and the risk of MACE. Least absolute shrinkage and selection operator (LASSO) penalized regression evaluated 19 prespecified candidate predictors using 10-fold cross-validation and the one-standard-error rule. A nomogram was constructed based on the selected factors, and the model’s discriminatory power, consistency, and clinical net benefit were evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Internal validation used 1,000 bootstrap resamples that repeated the complete preprocessing, variable-selection, and model-refitting pipeline. A total of 138 patients with borderline lesions were enrolled (33 in the MACE group and 105 in the non-MACE group). Among 33 first MACE events, 20 were ischemia-driven revascularizations, 9 were non-fatal myocardial infarctions, and 4 were all-cause deaths. RCS analysis showed an overall association between lower 3V-QFR and higher MACE risk, whereas evidence for nonlinearity was not statistically strong; multivariable regression supported 3V-QFR as an independent predictor. LASSO regression identified four optimal predictors of 2-year MACE through 10-fold cross-validation: 3V-QFR, age, diabetes, and NT-proBNP levels. The final model included 3V-QFR, age, diabetes, and NT-proBNP. The apparent AUC was 0.869 (95% CI 0.802–0.936), and the optimism-corrected AUC was 0.842 (95% CI 0.771–0.913); calibration and decision-curve findings were considered exploratory. Lower 3V-QFR was associated with higher 2-year MACE risk in this retrospective development cohort. The 3V-QFR-based nomogram showed promising internal performance and may support exploratory risk stratification; independent external and prospective validation is required before clinical use.
